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  Prediction of enzyme kinetic parameters based on statistical learning

Borger, S., Liebermeister, W., & Klipp, E. (2006). Prediction of enzyme kinetic parameters based on statistical learning. Genome Informatics, 17(1), 80-87.

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 Creators:
Borger, Simon1, Author
Liebermeister, Wolfram1, Author
Klipp, Edda2, Author           
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1Max Planck Society, ou_persistent13              
2Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433554              

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Free keywords: enzyme parameter; Michaelis-Menten constant; regression model; statistical learning
 Abstract: Values of enzyme kinetic parameters are a key requisite for the kinetic modelling of biochemical systems. For most kinetic parameters, however, not even an order of magnitude is known, so the estimation of model parameters from experimental data remains a major task in systems biology. We propose a statistical approach to infer values for kinetic parameters across species and enzymes making use of parameter values that have been measured under various conditions and that are nowadays stored in databases. We fit the data by a statistical regression model in which the substrate, the combination enzyme-substrate and the combination organism-substrate have a linear effect on the logarithmic parameter value. As a result, we obtain predictions and error ranges for unknown enzyme parameters. We apply our method to decadic logarithmic Michaelis-Menten constants from the BRENDA database and confirm the results with leave-one-out crossvalidation, in which we mask one value at a time and predict it from the remaining data. For a set of 8 metabolites we obtain a standard prediction error of 1.01 for the deviation of the predicted values from the true values, while the standard deviation of the experimental values is 1.16. The method is applicable to other types of kinetic parameters for which many experimental data are available.

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Language(s): eng - English
 Dates: 2006
 Publication Status: Issued
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 Identifiers: eDoc: 307414
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Title: Genome Informatics
Source Genre: Journal
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Pages: - Volume / Issue: 17 (1) Sequence Number: - Start / End Page: 80 - 87 Identifier: ISSN: 0919-9454